Investigation of the Effects of Imputation Methods for Gene Regulatory Networks Modelling Using Dynamic Bayesian Networks
DNA microarray technology plays an important role in advancing the analysis of gene expression and gene functions. However, gene expression data often contain missing values, which cause problems as most of the analysis methods of gene expression data require a complete matrix. Several missing value...
| Main Authors: | , , , , , , , , , , |
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| Format: | Book Chapter |
| Language: | English |
| Published: |
Springer International Publishing
2016
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/13918/1/Investigation%20of%20the%20Effects%20of%20Imputation%20Methods%20for%20Gene%20Regulatory%20Networks%20Modelling%20Using%20Dynamic%20Bayesian%20Networks.pdf |
| _version_ | 1825823070111137792 |
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| author | Sin, Yi Lim Mohd Saberi, Mohamad Lian, En Chai Safaai, Deris Weng, Howe Chan Sigeru, Omatu Muhammad Farhan, Sjaugi Muhammad Mahfuz, Zainuddin Gopinathaan, Rajamohan Zuwairie, Ibrahim Zulkifli, Md. Yusof |
| author_facet | Sin, Yi Lim Mohd Saberi, Mohamad Lian, En Chai Safaai, Deris Weng, Howe Chan Sigeru, Omatu Muhammad Farhan, Sjaugi Muhammad Mahfuz, Zainuddin Gopinathaan, Rajamohan Zuwairie, Ibrahim Zulkifli, Md. Yusof |
| author_sort | Sin, Yi Lim |
| collection | UMP |
| description | DNA microarray technology plays an important role in advancing the analysis of gene expression and gene functions. However, gene expression data often contain missing values, which cause problems as most of the analysis methods of gene expression data require a complete matrix. Several missing value imputation methods have been developed to overcome the problems. In this paper, effects of the missing value imputation methods in modeling of gene regulatory network are investigated. Three missing value imputation methods are used, which are k-Nearest Neighbor (kNN), Iterated Local Least Squares (ILLsimpute), and Fixed Rank Approximation Algorithm (FRAA). Dataset used in this paper is E. coli. The results suggest that the performance of each missing value imputation method is influenced by the percentage and distribution of the missing values in the dataset, which subsequently affect the modeling of gene regulatory network using Dynamic Bayesian network. |
| first_indexed | 2024-03-06T12:05:55Z |
| format | Book Chapter |
| id | UMPir13918 |
| institution | Universiti Malaysia Pahang |
| language | English |
| last_indexed | 2024-03-06T12:05:55Z |
| publishDate | 2016 |
| publisher | Springer International Publishing |
| record_format | dspace |
| spelling | UMPir139182018-02-08T02:54:32Z http://umpir.ump.edu.my/id/eprint/13918/ Investigation of the Effects of Imputation Methods for Gene Regulatory Networks Modelling Using Dynamic Bayesian Networks Sin, Yi Lim Mohd Saberi, Mohamad Lian, En Chai Safaai, Deris Weng, Howe Chan Sigeru, Omatu Muhammad Farhan, Sjaugi Muhammad Mahfuz, Zainuddin Gopinathaan, Rajamohan Zuwairie, Ibrahim Zulkifli, Md. Yusof TK Electrical engineering. Electronics Nuclear engineering TS Manufactures DNA microarray technology plays an important role in advancing the analysis of gene expression and gene functions. However, gene expression data often contain missing values, which cause problems as most of the analysis methods of gene expression data require a complete matrix. Several missing value imputation methods have been developed to overcome the problems. In this paper, effects of the missing value imputation methods in modeling of gene regulatory network are investigated. Three missing value imputation methods are used, which are k-Nearest Neighbor (kNN), Iterated Local Least Squares (ILLsimpute), and Fixed Rank Approximation Algorithm (FRAA). Dataset used in this paper is E. coli. The results suggest that the performance of each missing value imputation method is influenced by the percentage and distribution of the missing values in the dataset, which subsequently affect the modeling of gene regulatory network using Dynamic Bayesian network. Springer International Publishing 2016 Book Chapter PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/13918/1/Investigation%20of%20the%20Effects%20of%20Imputation%20Methods%20for%20Gene%20Regulatory%20Networks%20Modelling%20Using%20Dynamic%20Bayesian%20Networks.pdf Sin, Yi Lim and Mohd Saberi, Mohamad and Lian, En Chai and Safaai, Deris and Weng, Howe Chan and Sigeru, Omatu and Muhammad Farhan, Sjaugi and Muhammad Mahfuz, Zainuddin and Gopinathaan, Rajamohan and Zuwairie, Ibrahim and Zulkifli, Md. Yusof (2016) Investigation of the Effects of Imputation Methods for Gene Regulatory Networks Modelling Using Dynamic Bayesian Networks. In: Distributed Computing and Artificial Intelligence, 13th International Conference. Springer International Publishing, pp. 413-421. ISBN 978-3-319-40161-4 http://link.springer.com/chapter/10.1007/978-3-319-40162-1_45 DOI: 10.1007/978-3-319-40162-1_45 |
| spellingShingle | TK Electrical engineering. Electronics Nuclear engineering TS Manufactures Sin, Yi Lim Mohd Saberi, Mohamad Lian, En Chai Safaai, Deris Weng, Howe Chan Sigeru, Omatu Muhammad Farhan, Sjaugi Muhammad Mahfuz, Zainuddin Gopinathaan, Rajamohan Zuwairie, Ibrahim Zulkifli, Md. Yusof Investigation of the Effects of Imputation Methods for Gene Regulatory Networks Modelling Using Dynamic Bayesian Networks |
| title | Investigation of the Effects of Imputation Methods for Gene Regulatory Networks Modelling Using Dynamic Bayesian Networks |
| title_full | Investigation of the Effects of Imputation Methods for Gene Regulatory Networks Modelling Using Dynamic Bayesian Networks |
| title_fullStr | Investigation of the Effects of Imputation Methods for Gene Regulatory Networks Modelling Using Dynamic Bayesian Networks |
| title_full_unstemmed | Investigation of the Effects of Imputation Methods for Gene Regulatory Networks Modelling Using Dynamic Bayesian Networks |
| title_short | Investigation of the Effects of Imputation Methods for Gene Regulatory Networks Modelling Using Dynamic Bayesian Networks |
| title_sort | investigation of the effects of imputation methods for gene regulatory networks modelling using dynamic bayesian networks |
| topic | TK Electrical engineering. Electronics Nuclear engineering TS Manufactures |
| url | http://umpir.ump.edu.my/id/eprint/13918/1/Investigation%20of%20the%20Effects%20of%20Imputation%20Methods%20for%20Gene%20Regulatory%20Networks%20Modelling%20Using%20Dynamic%20Bayesian%20Networks.pdf |
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